Human Centric Lighting in Lighting Control Systems

The Dutch funding agency STW has approved the ILI project proposal Optilight. This project will strengthen the cooperation between various Lighting-oriented groups at TU/e. At the moment the project is searching PhD candidates who are interesting in mathematical optimization for human centric lighting.

From experiments to theory to algorithms

The project aims to make lighting control systems more centered towards the human user. This requires not only better insights in how humans experience light but also demands quantified models and optimization algorithms that are executed by automated lighting control systems. Despite the growing scientific understanding of the impact of light on, for instance, wellbeing, performance, circadian rhythms and sleep, benefits of this understanding cannot (yet) easily be harvested in practical systems. We lack scalable algorithms that can be used in automated systems and that can be deployed in different environments without extensive tuning by experienced lighting experts. Scalability towards broad deployment is a key sub goal of this project.

Humans want to experience light as a natural given. Having to adjust the light setting regularly is not attractive. Moreover, people are usually not aware of the longer-term effects of light so they don’t not necessarily select the optimal light setting. On the other hand, automatic controls often fail to offer a comfortable and unobtrusive natural experience and even tend to irritate people. Hence, there exists a huge gap between results obtained in controlled environments and practical deployment.

Capturing human perception in mathematical models

Although control theory and optimizations using statistical signal processing are well established areas, to date they not widely used for lighting control. Yet, in audio and video processing and in gaming, models on human factors are successfully being used by automated systems. Hence the team is confident that better models human on experience and perception can improve automatic control systems. However, it is not straightforward to capture human experience in equations. We believe that an important step is to better quantify the reliability of such expressions and to take this into account in probabilistic algorithms.


This project will bring together experts from Electrical Engineering, Architecture and Human Technology Interaction. Prof. Jean-Paul Linnartz of the Signal Processing and Systems group at EE will lead the overall project. PhD student in his team will apply proposed models of human experience to allow automated optimizations. Prof. Alexander Rosemann and his team studying the Built Environment (Architecture) will analyze data collected in real environments and compare this to data from fully controlled situations. Prof. Yvonne de Kort and Dr. Ingrid Vogels bring in expertise on the impact of light on human functioning and visual experience and comfort. Dr. Tanir Ozcelebi of System Architecture and Networking group in Computer Sciences brings in his expertise on programming intelligent and learning behaviour of smart space applications.


Contact: Prof. Jean-Paul Linnartz

j.p.linnartz (at)

EnLight wins ENIAC Innovation award


Participation in large European projects not only gives ILI first hand insights in the system architecture of future lighting control systems, it also is a good way transfer insights and knowledge to many industrial partners. This was particularly rewarding in the large EnLight project, with partners ranging from device suppliers, software and integration specialists to system developers. This project has been awarded with the ENIAC Innovation Award.

The EU applauded how EnLight was exemplary in bringing together key actors in a project of significant size (more than 41M€ R&D investment by 27 partners) to achieve results of genuinely high value to the partners. It highlights the importance of semiconductor technology as a core European competence, which fully delivers on its promise of innovation when taken up by leading actors along the full value chain.

In EnLight, the Signal Processing Systems group at TU/e EE cooperated closely with for instance Philips and NXP. The project had three technical objectives namely the optimization of LED lighting modules, the design of future luminaires and the use of new, intelligent lighting systems. As a result, the energy savings in office applications could be shown at 44% compared to LED retrofit and standard controlled lighting systems. The energy savings in hospitality could nearly be doubled and ended up at a figure of 81% energy reduction by using new luminary designs with intelligent controls. This motivated the journal LED-Professional to devote a full special issue to this project.

Decision rule engine

The EnLight project has given the TU/e PhD candidates Xin Wang and Amir Jalalirad an excellent view of the true problems in the future lighting installations. During the project they refined the initial specification of the rules and implemented the engine on the NXP Jennic platform. The insights they obtained in the requirements of lighting systems were augmented by the intelligent control implemented in the EnLight demonstrations in Oulu (VTT, Finland), Munich (Osram, Germany), and Eindhoven (Philips, The Netherlands).
Scientific Directions
Now in the final year of their PhD project, with the EnLight experience behind them, Xin and Amir are focusing on the next wave of innovation, particularly from the insights of data analytics and optimized control.

In EnLight, the industry has set the stage for an architecture that allows intelligent and energy saving applications to be executed. Prof. Jean-Paul Linnartz, advisor of the two PhD candidates in EnLight, sees the harmonization of a rule engine as a good step forward in EnLight. Particularly the option to adapt rules as the systems learns about its users and its environments enables further innovation towards self-adapting systems. “But in the long run, we may have to extent if-then-else rules with probabilistic optimizations”. “Practical systems will never have absolute knowledge about what human users are preferring. Hence these systems should optimize light setting according to a cost function, rather than make hard choices”. In some of his recent papers Xin Wang has worked this out by modelling of human satisfaction and, for instance, energy consumption in mathematical models. For the human experience that required the inclusion of uncertainty. This avoids to a large extent the annoying wrong decisions that current automatic systems make when sensors are not working perfectly.

Dr. Tjalling Tjalkens, also coaching the PhD candidates at the faculty of Electrical Engineering sees a clear connection between machine learning, information theory and future lighting control. Yet we have to advance the scientific state-of-art, because in well-functioning lighting control, the number of human interventions should be very minimal. Hence such system have only few learning opportunities, much less than academic machine learning algorithms typically require.




Wireless Communication course material


Starting with a U.C. Berkeley course on wireless communication in the early 90s, I collected material on radio communication principles and systems. It has been published as a multi-media CD-ROM by Kluwer and later by Springer, but is now also available on the Internet. Although admittedly some of the material on wireless systems is a bit outdated, the physics of propagation and multipath channels, as well as the basics of multiple access schemes remain highly relevant.